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1. Linear_Programming.ipynb Linear_Programming.ipynb

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               "source": "# Tutorial: Linear Programming, (CPLEX Part 1)\n\n\nThis notebook gives an overview of Linear Programming (or LP). After completing this unit, you should be able to \n- describe the characteristics of an LP in terms of the objective, decision variables and constraints, \n- formulate a simple LP model on paper, \n- conceptually explain some standard terms related to LP, such as dual, feasible region, infeasible, unbounded, slack, reduced cost, and degenerate.  \n    \nYou should also be able to describe some of the algorithms used to solve LPs, explain what presolve does, and recognize the elements of an LP in a basic DOcplex model.\n\n\n>This notebook is part of IBM\u00ae Decision Optimization (Prescriptive Analystics) CPLEX\u00ae Modeling for Python** http://ibmdecisionoptimization.github.io/docplex-doc/**\n>\n>It requires either an [installation of CPLEX Optimizers](http://ibmdecisionoptimization.github.io/docplex-doc/getting_started.html) or it can be run on [IBM Watson Studio Cloud](https://www.ibm.com/cloud/watson-studio/>) (Sign up for a [free IBM Cloud account](https://dataplatform.cloud.ibm.com/registration/stepone?context=wdp&apps=all>)\nand you can start using Watson Studio Cloud right away).\n\n\nTable of contents:\n\n* [Introduction to Linear Programming](#Introduction-to-Linear-Programming)\n* [Example: a production problem](#Example:-a-production-problem)\n* [CPLEX Modeling for Python](#Use-IBM-Decision-Optimization-CPLEX-Modeling-for-Python)\n* [Algorithms for solving LPs](#Algorithms-for-solving-LPs)\n* [Summary](#Summary)\n* [References](#References)", 

2. Data Science - DOcplex/Python (Trans... Data Science - DOcplex/Python (Transportation Problem)

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               "source": "# Linear Programming CPLEX/Python (Transportation Problem)\n\nThis notebook solves a simple network transportation problem using Decision Optimization (Prescriptive Analytics). IBM DOcplex is used to formulate the mathematical model in Python which is then immediately solved.\n\nJames Allan May 29, 2019\n\nSupply nodes (1,2) with capacities (15,20)\nTarget nodes (3,4,5) with demands (7,10,15)\nArc (1-->3) has cost 2\nArc (1-->5) has cost 4\nArc (2-->4) has cost 5\nArc (2-->5) has cost 3\nOBJECTIVE: Minimize the total cost of transportation from the 2 supply nodes to the 3 demand nodes, so as to satisfy the demand, and without exceeding the suppliers' capacities. ", 

3. CP0101EN DOcplex/Python (Decision Op... CP0101EN DOcplex/Python (Decision Optimization)

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               "source": "# Linear Programming CPLEX/Python (Phone Problem)\n\n\nThis notebook solves a problem using Decision Optimization (Prescriptive Analytics). IBM \nDOcplex is used to formulate a mathematical model in Python which is then immediately solved.\n\nJames Allan May 28, 2019\n\n- Company must produce cell/desk phones (at least 100 of each) \n- takes 12/24 minutes to assemble a desk/cell phone\n- takes 30/24 minutes to paint a desk/cell phone\n- assembly time <= 400 hours\n- paint time <= 490 hours\n- profit is 12/20 (dollars) for desk/cell phone\n- OBJECTIVE: Maximize profit.  ", 

4. User-based Collaborative Recommendation User-based Collaborative Recommendation

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               "source": "\n# User-based Recommender using Collaborative Filtering", 

5. CP0101EN - Beyond_Linear_Programming CP0101EN - Beyond_Linear_Programming

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               "source": "# Tutorial: Beyond Linear Programming, (CPLEX Part2)\n\nThis notebook describes some special cases of LP, as well as some other non-LP techniques, and also under which conditions they should be used. \n\nBefore continuing, you should ensure you followed the CPLEX Tutorial Part 1.\n\nAfter completing this unit, you should be able to describe what a network model is, and the benefits of using network models, explain the concepts of nonlinearity and convexity, describe what a piecewise linear function is, and describe the differences between Linear Programming (LP), Integer Programming (IP), Mixed-Integer Programming (MIP),  and Quadratic Programming (QP).  You should also be able to construct a simple MIP model. \n\n>This notebook is part of **[Prescriptive Analytics for Python](http://ibmdecisionoptimization.github.io/docplex-doc/)**\n>\n>It requires either an [installation of CPLEX Optimizers](http://ibmdecisionoptimization.github.io/docplex-doc/getting_started.html) or it can be run on [IBM Watson Studio Cloud](https://www.ibm.com/cloud/watson-studio/>) (Sign up for a [free IBM Cloud account](https://dataplatform.cloud.ibm.com/registration/stepone?context=wdp&apps=all>)\nand you can start using Watson Studio Cloud right away).\n\n\nTable of contents:\n\n* [CPLEX Modeling for Python](#Use-IBM-Decision-Optimization-CPLEX-Modeling-for-Python)\n* [Network models](#Network-models)\n* [Non-linearity and Convexity](#Non-linearity-and-Convexity)\n* [Integer Optimization](#Integer-Optimization)\n* [Quadratic Programming](#Quadratic-Programming)\n\nWe will use DOcplex to write small samples to illustrate the topics", 

6. Content Recommendation System (Movies) Content Recommendation System (Movies)

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               "source": "# Content Based Recomendation - with Python",